{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b037232f",
   "metadata": {},
   "source": [
    "# 决策树模型\n",
    "## 决策树模型简介\n",
    "决策树模型本质上一类有监督学习模型，与传统统计学模式背景下的一般线性模型、广义线性模型甚至是判别分析不同，决策树在工作过程中通过一系列推理规则从根结点出发生成一棵二叉或者是多叉树，这棵树最后的叶子结点将会对应最终的各个分类项、或是回归值。<br>\n",
    "从决策树的工作模式出发，我们不难看出他不受到传统统计学分类模型中对数据分布的强要求；与此同时，树生成过程中的每一次分叉实际上都是在样本的多维属性空间中划分出一个超平面，<font color=blue><b>特别的，该超平面一定是垂直于该层推理规则所涉及属性的系列坐标轴（二元假设推断或多元假设推断）或与该系列坐标轴呈一定夹角（线性划分标准）</b></font>，因此决策树的分类平面将会是多个超平面的叠加组合，这也从一定程度上超越了线性分类器的分类性能，使其能够解决诸如异或不可分的难题而不用使用降维或者是核函数等数学变换手段<br>\n",
    "\n",
    "决策树在树生成过程中，每一个中间节点内的数据量是不断减少的，而根节点将会包含全部数据；中间节点中的数据实质上是符合从根结点到该中间节点路径上所有推理｜判别规则的集合，即便他们的标签可能不是同质的。<br>\n",
    "与诸多机器学习模型一样，决策树模型能够解决分类和回归的问题。在解决分类问题时，叶子节点的预测值是该叶子节点中所有数据标签的众数，预测的置信度即为节点中众数标签数据所占全部数据的百分比；在解决回归问题时，叶子节点的预测值是该叶子节点中所有数据输出变量的平均数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e930fe",
   "metadata": {},
   "source": [
    "## 决策树模型核心问题\n",
    "当我们尝试生成一棵决策树时，我们需要思考两个关键流程\n",
    "1. 决策树的生长 - 分类划分标准以及停止生长策略\n",
    "2. 决策树的剪枝 - 防止过拟合的发生\n",
    "\n",
    "### 分类划分标准\n",
    "#### 信息增益\n",
    "能够让计算机读懂的划分标准一定是一个能够进行比较的数值，因此我们在设计划分标准时需要将属于决策树中上一层的数据集根据其聚集特征产出一系列能够进行比较的数值，通过比较后寻找最优值对应的划分标准<br>\n",
    "\n",
    "由于我们的任务是寻找能够在最大程度上区分数据集的判断标准，因此我们不妨分属性进行寻找。根据信息论的观点，每个属性将向决策树上一层的数据集中引入新的信息量（模型中可以理解为条件概率），因此我们可以通过计算引入这个属性的分类信息后产生的互信息来衡量该属性对于减少上一层数据集信息熵的有效程度.某个属性引入后产生的互信息越大，说明引入该属性的信息后原数据集的数据熵越小，数据更趋于分类有序，这便意味着该属性将数据集分类的效果就越好\n",
    "\n",
    "首先，我们引入互信息的概念<br>\n",
    "互信息的计算公式为:\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "    I(X;Y) & = & E(X) - E(X|Y) \\\\\n",
    "    & = & \\sum p(x)log p(x) - \\sum\\sum p(x,y)log \\frac{p(x,y)}{p(x)p(y)}\n",
    "\\end{array}\n",
    "$$\n",
    "其中$E(X)$代表随机变量$X$的信息熵，$I(X;Y)$表示引入随机变量$Y$后$X$的信息增益 <br>\n",
    "依据互信息计算公式，我们能够定义<font color=\"red\"><b>信息增益</b></font>：\n",
    "$$\n",
    "    Gain(D,a) = E(D) - \\displaystyle{\\sum_{v=1}^{V}\\frac{|D^v|}{|D|}E(D^v)}\n",
    "$$\n",
    "在公式中，$D$表示决策树上一层数据集，$|D|$即为该数据集中样本数量，我们采用属性$a$对该数据集进行划分，<br>\n",
    "已知属性$a$有$V$个可能的取值$\\{a^1,a^2,\\cdots,a^V\\}$，在决策树中，每一个可能的取值将会对应该层的一个中间节点，<br>\n",
    "因此每一个分类取值$a^v$下的样本集将被定义为$D^v$，该样本集所含样本总数量即为$|D^v|$<br>\n",
    "\n",
    "公式中$\\frac{|D^v|}{|D|}$对应互信息中的$p(x,y)$项，<br>\n",
    "$E(D^v)$对应互信息中$\\sum\\frac{p(x,y)}{p(x)p(y)}$项\n",
    "\n",
    "在实际操作中，以上信息增益公式将会更好理解：<br>\n",
    "$\\frac{|D^v|}{|D|}$代表属性$a$的$a^v$分类下样本数量占全部样本数量的比值;<br>\n",
    "$E(D^v)$的计算对应$\\sum\\frac{p(x,y)}{p(x)p(y)}$项的计算，实际上就是在$a^v$分类下的数据集中不同标签的二项分布或V项分布的信息熵\n",
    "\n",
    "#### 增益率\n",
    "通过计算公式我们可以发现，信息增益更倾向于选择分类较多的属性，因此为了能够避免在此表现上出现过拟合的现象，我们引入信息增益率<br>\n",
    "信息增益率是一个双因子变量，其分母的作用即为抵消由于某一属性分类类别过多而造成数值过大的影响：\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "    & Gain\\_ratio(D,a) = \\displaystyle{\\frac{Gain(D,a)}{IV(a)}} \\\\ \n",
    "    & IV(a) = \\displaystyle{-\\sum_{v=1}^{V}\\frac{|D^v|}{|D|}log_2 \\frac{|D^v|}{|D|}} \\\\\n",
    "\\end{array}\n",
    "$$\n",
    "$IV(a)$的计算类似于熵的计算，当一个属性类别过多时，该值会变大<br>\n",
    "对待信息增益和信息增益率，我们的最优化问题是一个最大值问题：\n",
    "$$\n",
    "    a_* = \\mathop{\\arg \\max}\\limits_{a \\in A} Gain(D,a)\n",
    "$$\n",
    "\n",
    "#### 基尼系数\n",
    "由于信息熵的计算过于繁琐，在一般机器学习算法中，默认的分类指标是基尼系数<br>\n",
    "基尼系数从每个属性的不同类别整体出发，对不同类别中数据量占整体数量的百分比进行相乘运算。如果该属性划分数据集后每个类别内部数据纯净，那么基尼系数将为0；因此，基尼系数越小，数据集纯度越高<br>\n",
    "基尼值的定义如下：\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "    Gini(D) & = & \\displaystyle{\\sum_{k=1}^{|V|}\\sum_{k^\\prime \\ne k}p_k{p_k}^\\prime} \\\\\n",
    "    & = & 1- \\displaystyle{\\sum_{k=1}^{|V|}{p_k}^2} \\\\\n",
    "\\end{array}\n",
    "$$\n",
    "其中，$|V|$代表某个属性下所有分类的数量，我们计算的是$p_k = \\frac{|D^v|}{|D|}$<br>\n",
    "因此，我们可以定义基尼系数:\n",
    "$$\n",
    "    Gini\\_index(D,a) = \\displaystyle{\\sum_{v=1}^{V}  \\frac{|D^v|}{|D|} Gini(D^v)}\n",
    "$$\n",
    "对待基尼系数，我们的最优化问题是一个最小值问题：\n",
    "$$\n",
    "    a_* = \\mathop{\\arg \\min}\\limits_{a \\in A} Gini\\_index(D,a)\n",
    "$$\n",
    "### 停止生长策略\n",
    "由于编程过程中我们能够确定决策树的生长是一个递归的过程，因此算法中的三个流程将会导致递归返回：\n",
    "1. 当前节点包含所有样本属于同一个类别\n",
    "2. 当前的属性集为空\n",
    "3. 当前节点包含的样本集合为空\n",
    "\n",
    "### 剪枝策略\n",
    "决策树的剪枝包括两种不同的策略，分别是预剪枝和后剪枝<br>\n",
    "1. 预剪枝过程发生在决策树生成的过程之中，这也是我们在调用决策树算法时需要定义的超参数；<br>\n",
    "2. 后剪枝过程发生在决策树生成之后，我们从叶子节点出发，合并判断误差高于设定值的叶子节点，提升模型的泛化能力"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f5a0bb8",
   "metadata": {},
   "source": [
    "## 决策树 - python代码\n",
    "python sklearn.tree DecisionTreeClassifier 包含决策树模型<br>\n",
    "\n",
    "在这里我们需要介绍几个关键的超参数，这些超参数无一例外都是剪枝策略相关的参数<br>\n",
    "criterioin - 分类决策函数类型，默认是 Gini index <br>\n",
    "max_depth - 控制决策树的深度<br>\n",
    "min_samples_split - 控制每个叶子节点中样本的数量<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fe3e1d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test acc:0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings \n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "iris_X, iris_y = load_iris(return_X_y=True)\n",
    "df_clf = DecisionTreeClassifier(random_state=42,max_depth=5,min_samples_leaf=0.1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(iris_X, iris_y, test_size=0.2,random_state=42)\n",
    "df_clf.fit(x_train, y_train)\n",
    "y_pred = df_clf.predict(x_test)\n",
    "print(\"test acc:{}\".format(accuracy_score(y_pred,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c17d37e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00158406, 0.        , 0.99841594, 0.        ])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f4d9e5",
   "metadata": {},
   "source": [
    "### 结果解读1\n",
    "以上代码中，我们采用旁置法首先训练了一个决策树分类器，其次使用测试集对分类效果进行测试，结果显示对于测试集的预测准确率在96.67%\n",
    "\n",
    "### 决策树可视化\n",
    "sklearn同时支持决策树模型的可视化，我们通过tree.plot_tree函数进行可视化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af653f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt \n",
    "import sklearn.tree as tree\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "plt.rcParams[\"font.sans-serif\"] = \"SimHei\"\n",
    "\n",
    "feature_names = load_iris()[\"feature_names\"]\n",
    "target_names = load_iris()[\"target_names\"]\n",
    "\n",
    "plt.figure(figsize=(10,8), facecolor= 'whitesmoke')\n",
    "a = tree.plot_tree(df_clf,\n",
    "                    feature_names = feature_names,\n",
    "                    class_names= target_names,\n",
    "                    rounded=True,\n",
    "                    filled= True,\n",
    "                    fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1338ece",
   "metadata": {},
   "source": [
    "### CV - 留一法\n",
    "对于决策树的训练我们也可以采用留一法，也就是交叉检验的方法CV进行超参数的调参<br>\n",
    "对于cross_validate函数，我们能够选择的scoring函数可以通过以下指令进行查询：\n",
    "```python\n",
    "sorted(sklearn.metrics.SCORERS.keys())\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7d04c58b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([0.00079513, 0.00091505, 0.00044966, 0.00041008, 0.00038719]),\n",
       " 'score_time': array([0.00037408, 0.00015783, 0.00013113, 0.00012898, 0.00013113]),\n",
       " 'test_accuracy': array([0.93333333, 0.96666667, 0.9       , 0.86666667, 1.        ])}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score\n",
    "\n",
    "# first test decisiontree classifier\n",
    "dt_clf = DecisionTreeClassifier(random_state=42, max_depth=5, min_samples_leaf=0.1)\n",
    "cv_clf_score = cross_validate(dt_clf, X=iris_X, y=iris_y, cv=5, scoring=[\"accuracy\"])\n",
    "cv_clf_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "666a0a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.01800412, 0.14856771, 0.05398313, 0.17422525])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# then test decisiontree regressor\n",
    "dt_reg = DecisionTreeRegressor(random_state=42, max_depth=5, min_samples_leaf=0.1)\n",
    "cv_reg_score = cross_val_score(dt_reg,iris_X,iris_y,scoring=\"neg_mean_squared_error\")\n",
    "-cv_reg_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5586d8e4",
   "metadata": {},
   "source": [
    "# 集成学习\n",
    "集成学习通过构建并结合多个学习器来完成学习任务。简单来说，集成学习首先产生一组个体学习器，然后通过某种策略将这些个体学习器结合起来，最后通过对每一个个体学习器的输出结果进行整合，得出集成学习器的最终输出。<br>\n",
    "在这里我们需要明确几个概念，如果集成学习器中仅包含一种个体学习器，那么这些个体学习器被称为<font color=maroon><b>基学习器</b></font>，如果是多个不同类型的个体学习器，那么他们被称为<font color=maroon><b>组件学习器</b></font><br>\n",
    "\n",
    "通过思考我们可以不加证明的发现，个体学习器既要有一定的准确性，又要有一定的差异性，这样得出的集成学习器才能够有更好的效果<br>\n",
    "从数学角度而言，集成学习将多个学习器进行结合，其最终构成的集成学习器常常能够获得比单一学习器更优秀的泛化性能，下面我们简单以投票法集成的多个二分类器来证明以上理论。<br>\n",
    "\n",
    "考虑二分类问题$y \\in \\{-1,+1\\}$和真实函数$\\mathop{f}$，假定基分类器的错误率为$\\epsilon$，即对于每一个基分类器而言，有\n",
    "\n",
    "$$\n",
    "    P(h_i(x) \\ne \\mathop{f}(x)) = \\epsilon\n",
    "$$\n",
    "\n",
    "集成分类器$\\mathop{H}$将以投票法集成T个基分类器，这意味着只要有半数基分类器分类正确，集成分类正确：\n",
    "\n",
    "$$\n",
    "    \\mathop{H}(x) = sign(\\displaystyle\\sum_{i=1}^{T}h_i(x))\n",
    "$$\n",
    "\n",
    "以上，$sign()$函数是示例函数，它将完成半数投票的工作<br>\n",
    "假设基分类器的错误率相互独立，则由Hoeffding不等式可知，\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "    P(h(x) \\ne \\mathop{f}(x)) & = & \\displaystyle\\sum_{k=0}^{[T/2]}C^{T}_{k}{1-\\epsilon}^k\\epsilon^{T-k} \\\\\n",
    "    & \\le & exp(-\\frac{1}{2}T(1-2\\epsilon)^2)\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "从以上式子我们可以看出，随着基分类器数目$T$的增大，集成分类器的分类错误率将会呈指数级别下降\n",
    "\n",
    "## Boosting\n",
    "boosting的构建策略是：首先从初始训练集中训练出一个基学习器，再根据基学习器的表现对训练样本分布进行调整，使得前一个基学习器分类错误的训练样本在后续的训练过程中拥有更高的加权；接下来基于调整后的样本分布训练下一个基学习器；重复此过程直到基学习器数量达到指定的T值，最终将这T个基学习器的输出加权结合作为集成学习器的输出\n",
    "\n",
    "### AdaBoost\n",
    "AdaBoost是Boost算法思想最著名的代表 <br>\n",
    "考虑二分类问题$y \\in \\{-1,+1\\}$和真实函数$\\mathop{f}$，AdaBoost的加性模型数学表达形式如下：\n",
    "\n",
    "$$\n",
    "    H(x) = \\displaystyle\\sum_{i=1}^{T}{\\alpha_ih_i(x)}\n",
    "$$\n",
    "\n",
    "训练模型的最优化问题是最小化指数损失函数，但是需要注意，由于boosting算法具有很强的步骤性，因此最小化问题的闭式表达式较难求解\n",
    "\n",
    "$$\n",
    "    l_{exp}(H|\\mathbb{D}) = \\mathbb{E}_{x \\sim \\mathbb{D}}[e^{-\\mathop{f}(x)H(x)}]\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e90125d5",
   "metadata": {},
   "source": [
    "## AdaBoost - python代码\n",
    "首先，adaboost较少在实际应用中使用，<br>\n",
    "sklearn.ensemble AdaBoostClassifier集成了AdaBoost算法<br>\n",
    "该算法中几个超参数需要我们进行调整：<br>\n",
    "base_estimator - 基学习器<br>\n",
    "n_estimators - 基学习器数量T<br>\n",
    "learning_rate - 初始学习器权值<br>\n",
    "\n",
    "注意，基于算法实现，每一个基学习器都需要满足计算每个分类概率的方法，即需要有proba_属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0bd4039c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_iris\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "iris_X, iris_y = load_iris(return_X_y=True)\n",
    "x_train, x_test, y_train, y_test = train_test_split(iris_X,iris_y,test_size=0.2,random_state=42)\n",
    "base_clf = DecisionTreeClassifier(max_depth=2,min_samples_leaf=0.3,random_state=42)\n",
    "ada_clf = AdaBoostClassifier(base_estimator=base_clf,n_estimators=50,random_state=42)\n",
    "ada_clf.fit(x_train,y_train)\n",
    "y_pred = ada_clf.predict(x_test)\n",
    "print(accuracy_score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145f85b0",
   "metadata": {},
   "source": [
    "## Bagging\n",
    "相比Boosting，Bagging(Boostrap aggregation)在训练过程中使用的数据集将是不同的，这样能够从数据集的角度保证不同基分类器之间的独立性<br>\n",
    "为了达到不同基分类器的训练数据做到最大程度上的独立，Bagging算法采用<font color=maroon><b>自助采样法(bootstrap sampling)</b></font>对数据集进行采样，简单来说就是从整体样本集中有放回的抽取固定数量的样本集<br>\n",
    "在基分类器集成的策略上Bagging通常在分类问题上采取简单投票法，在回归问题上采取简单平均法<br>\n",
    "\n",
    "Bagging算法中应用最为广泛的模型即为决策树模型组合而成的随机森林模型，下面我们将单独重点介绍随机森林模型"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4901bc17",
   "metadata": {},
   "source": [
    "# 随机森林\n",
    "本文采用最简单的方式描述随机森林模型<br>\n",
    "随机森林可以通过哥基分类器的简单加合作为集成分类器的整体结构，在数学公式中，我们可以简单地将最后模型的预测输出表示为\n",
    "\n",
    "$$\n",
    "    \\hat y = \\displaystyle{\\sum_{t=1}^{T}\\mathop{f_t}(x)}\n",
    "$$\n",
    "\n",
    "其中，T如前文一样表示基分类器数量，$\\mathop{f_t}$表示第t个基学习器模型<br>\n",
    "接下来，对于模型在每一次迭代更新时的更新方式便造就了GBDT和XGBoost的不同之处<br>\n",
    "\n",
    "从相同之处出发，两种随机森林算法都是通过计算上一轮预测值与实际值的残差，并将该残差作为新的数据点进行下一轮基分类器进行拟合，二者最大的不同之处在于对待残差的处理和表示方式\n",
    "\n",
    "## GBDT\n",
    "对于GBDT而言，我们采取损失函数的负梯度作为两论模型预测值之间的残差，大致的计算公式可以表示如下：\n",
    "\n",
    "$$\n",
    "    \\mathop{f_t}(x) = \\hat y_t - \\hat y_{t-1} = - \\eta \\frac{\\partial l(y,\\hat y)}{\\partial \\hat y}\n",
    "$$\n",
    "其中$ \\eta$代表学习率，$\\hat y_t$代表第t轮标签值，$\\hat y_{t-1}$代表第(t-1)轮标签值\n",
    "\n",
    "## XGboost\n",
    "\n",
    "在GXGBoost中，我们对损失函数进行二姐泰勒展开，同时将一阶导数和二阶导数作为残差带入下一轮的拟合过程进行学习，简单来说，其损失函数的数学表达式可以表示如下：\n",
    "\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "    L^{(t)} & = &  \\displaystyle{\\sum_{i=1}^{n}l(y_i,{\\hat y_i}^{(t-1)}+\\mathop{f_i}(x_i))} + \\Omega(\\mathop{f_t}) \\\\\n",
    "    \\Omega(\\mathop{f_t})  & = & \\gamma J + \\frac{\\lambda}{2} \\displaystyle{}\\sum_{j=1}^{J}{\\omega_{tj}}^2 \\\\\n",
    "\\end{array}\n",
    "$$\n",
    "\n",
    "其中，$ \\Omega(\\mathop{f_t})$是正则项，能够惩罚叶子节点较多的决策树模型，<br>\n",
    "$J$为某个模型叶子节点的数量，$\\omega_{tj}$是第j个叶子节点的输出,$\\gamma$和$\\lambda$均为与模型无关的学习速率<br>\n",
    "\n",
    "以上公式中我们可以明显看出，第t轮模型所拟合的是(t-1)轮模型输出的值与实际值的残差${\\hat y_i}^{(t-1)} - y_i$<br>\n",
    "将以上公式进行二阶泰勒展开，我们可以得到：\n",
    "\n",
    "$$\n",
    "\\left\\{\n",
    "\\begin{array}{ll}\n",
    "    L^{(t)} & \\approx & \\displaystyle{\\sum_{i=1}^{n}[l(y_i,{\\hat y_i}^{(t-1)}) + g_i\\mathop{f_t}(x_i) + \\frac{1}{2}h_i\\mathop{{f_t}^2}(x_i)]} + \\Omega(\\mathop{f_t}) \\\\\n",
    "    g_i & = & \\partial_{\\hat y^{(t-1)}}l(y_i,{\\hat y_i}^{(t-1)}) \\\\\n",
    "    h_i & = & \\partial^2_{\\hat y^{(t-1)}}l(y_i,{\\hat y_i}^{(t-1)}) \\\\\n",
    "\\end{array}\n",
    "\\right .\n",
    "$$\n",
    "\n",
    "通过使得$\\frac{\\partial  L^{(t)} }{ \\partial \\hat y^{(t-1)}} = 0$ 我们可以求得t轮决策树相关的权值$\\{\\omega_{tj}\\}$，该轮训练结束<br>\n",
    "\n",
    "XGBoost使用了一阶和二阶偏导, 二阶导数有利于梯度下降的更快更准. 使用泰勒展开取得函数做自变量的二阶导数形式, 可以在不选定损失函数具体形式的情况下, 仅仅依靠输入数据的值就可以进行叶子分裂优化计算, 本质上也就把损失函数的选取和模型算法优化/参数选择分开了. 这种去耦合增加了XGBoost的适用性, 使得它按需选取损失函数, 可以用于分类, 也可以用于回归"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887b2f43",
   "metadata": {},
   "source": [
    "## XGBoost - python 代码\n",
    "XGBoost 是sklearn.ensemble RandomForestClassifier的默认使用算法，其重要的超参数与RandomForest类似:<br>\n",
    "n_estimators - 基分类器数量 <br>\n",
    "criterioin - 分类决策函数类型，默认是 Gini index <br>\n",
    "max_depth - 控制决策树的深度<br>\n",
    "min_samples_split - 控制每个叶子节点中样本的数量<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "869d4b7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import warnings \n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "iris_x, iris_y = load_iris(return_X_y=True)\n",
    "x_train, x_test, y_train, y_test = train_test_split(iris_X, iris_y, test_size=0.2, random_state=42)\n",
    "rf_clf = RandomForestClassifier(n_estimators=50, max_depth=2, min_samples_leaf=0.25,random_state=42)\n",
    "rf_clf.fit(x_train,y_train)\n",
    "y_pred = rf_clf.predict(x_test)\n",
    "print(accuracy_score(y_pred,y_test))"
   ]
  }
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